Machine learning to automate outfit advice

At Chicisimo, we are building an automated personal stylist that learns your clothing habits and understands your fashion taste. It then helps you decide your daily outfit, and tells you how new clothes match your existing ones.

In order to achieve this, we are building taste-capturing technology. We are focused on capturing people's clothing habits, interpreting captured data, and understanding the pain points to truly help people.

We’ve come to the conclusion that the single biggest opportunity in the fashion space is yet to be realized, and it’s about helping people feel better with their clothes. Simply focusing on selling more clothes to people is, in our opinion, focusing on the small opportunity.

Helping people feel better requires to own a mechanism to understand people's clothing habits in a clean and structured way. And it is on top of this clean data where you can build a truly automated personal stylist that has an impact on people's wellness. Offering such a service will let the winner own people’s attention, and so many more things as a result. That’s where we are focused, and that’s our purpose.

Our enabling infrastructure contains four assets:

1. A consumer app where people store their clothes, and see outfits uploaded by other people wearing those same clothes. Read more below;

2. A data platform that receives the data provided by people through our mobile app. With this data, the platform produces a dataset of clean, structured and correlated data that technology can interpret, and offer services on top of it. This platform is called the Social Fashion Graph, and it includes a fashion ontology that plays a key role. Read more below;

3. A dataset of correlated descriptors, outfits and people. These descriptors are a list of people’s what-to-wear needs, expressed in different forms. This dataset is exposed via a dataportal, which provides us with transparency and control. Read more below;

4. An IP portfolio protecting our innovations: tagging images with shoppable products; extracting correlations among clothes, in outfits and closets; outfits search. Read more here.

In the near future, a “Spotify for fashion” will exist. The technology behind such a service will focus on what we described above: tech to capture clothing habits, to interpret them, and to help people feel better.

The Social Fashion Graph

The Social Fashion Graph is the name we’ve given to our data platform. It learns about people’s what-to-wear needs, and attaches those needs to outfits and to people.

The backbone of the Social Fashion Graph is our ontology, which plays a critical role at giving structure to the incoming data.

We’ve learnt that the top need we all have is how to wear the specific clothes that we have in our closets, together with everyday special occasions, adapted to our characteristics. Dealing with these needs is the job of the Social Fashion Graph, once data is transfered from the app.

The result of the above is a dataset of clean, structured and correlated data that technology can interpret.

We’ve exposed all this data thru a dataportal, which has provided the team with transparency and control. As a result, it’s easier to understand where we are, and what’s next.

We do think that machine learning and deep learning in fashion are going to make people’s lives better. We are focused on the entire infrastructure and operations to ship this to people, and on having people be part of the infrastructure.

Our consumer app

Chicisimo’s iPhone and Android apps connect the Social Fashion Graph to the reality of people’s needs, and captures clothing data.

Most important of all, the apps help us learn, they bring unique impact to our learning process. Thanks to our app, we receive daily direct feedback from many people, which helps us learn.

We think this is the most interesting aspect of building a consumer product. The fact that, regularly, we access new corpuses of knowledge that we did not have before. This new knowledge helps us improve the tech and product significantly, and it is a great reminder that we are not in the upper part of the learning curve -we are simply moving up.

When we’ve obtained these game-changing learnings, it’s always been by focusing on two aspects: how people relate to the problem, and how people relate to solutions.

Iterating a consumer app is a unique learning experience.

Further reading:

- Learn about our learning process this Medium piece;

- Read Apple’s description of Chicisimo as “your dedicated personal stylist”;

- And finally, this is just a test for an outfit maker app, an outfit app, a closet app, an outfit planner app, or a wardrobe app.

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